Research on fusion of SAR image and multispectral image using texture feature information

2021 ◽  
Author(s):  
Kunpeng Wu ◽  
Lingjia Gu ◽  
Mingda Jiang
Keyword(s):  
Texture Feature ◽  
Multispectral Image ◽  
Sar Image ◽  
Feature Information

scholarly journals Multi-Feature Fusion and Adaptive Kernel Combination for SAR Image Classification

2021 ◽  
Vol 11 (4) ◽  
pp. 1603
Author(s):  
Xiaoying Wu ◽  
Xianbin Wen ◽  
Haixia Xu ◽  
Liming Yuan ◽  
Changlun Guo
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Feature Fusion ◽  
Texture Feature ◽  
Sar Image ◽  
Local Consistency ◽  
Sensing Applications ◽  
Adaptive Kernel ◽  
Feature Information ◽  
Kernel Combination

Synthetic aperture radar (SAR) image classification is an important task in remote sensing applications. However, it is challenging due to the speckle embedding in SAR imaging, which significantly degrades the classification performance. To address this issue, a new SAR image classification framework based on multi-feature fusion and adaptive kernel combination is proposed in this paper. Expressing pixel similarity by non-negative logarithmic likelihood difference, the generalized neighborhoods are newly defined. The adaptive kernel combination is designed on them to dynamically explore multi-feature information that is robust to speckle noise. Then, local consistency optimization is further applied to enhance label spatial smoothness during classification. By simultaneously utilizing adaptive kernel combination and local consistency optimization for the first time, the texture feature information, context information within features, generalized spatial information between features, and complementary information among features is fully integrated to ensure accurate and smooth classification. Compared with several state-of-the-art methods on synthetic and real SAR images, the proposed method demonstrates better performance in visual effects and classification quality, as the image edges and details are better preserved according to the experimental results.

A virtual seed file: the use of multispectral image analysis in the management of genebank seed accessions

2015 ◽  
Vol 14 (3) ◽  
pp. 238-241 ◽  
Author(s):  
Michael Adsetts Edberg Hansen ◽  
Fiona R. Hay ◽  
Jens Michael Carstensen
Keyword(s):  
Image Analysis ◽  
Multivariate Analysis ◽  
Vision System ◽  
Multispectral Image ◽  
Feature Information ◽  
Rice Seeds

We present a method for multispectral seed phenotyping as a fast and robust tool for managing genebank accessions. A multispectral vision system was used to take images of the seeds of 20 diverse varieties of rice (approximately 30 seeds for each variety). This was followed by extraction of feature information from the images. Multivariate analysis of the feature data was used to classify seed phenotypes according to accession. The proportion of correctly classified rice seeds was 93%. We conclude that the multispectral image analysis could play a role in comparing incoming seeds against existing accessions, identifying different seed types within a sample of seeds and/or in checking whether regenerated seeds match the original seeds.

An efficient method of SAR image segmentation based on texture feature

2017 ◽  
Vol 16 (4) ◽  
pp. 855-864 ◽  
Author(s):  
Xiaorong Xue ◽  
Jipeng Wang ◽  
Fang Xiang ◽  
Hongfu Wang
Keyword(s):  
Image Segmentation ◽  
Efficient Method ◽  
Texture Feature ◽  
Sar Image ◽  
Sar Image Segmentation

Building Damage Information Extraction From Fully Polarimetric SAR Images Based on Variogram Texture Feature

2020 ◽  
Author(s):  
Wei Zhai ◽  
Xiu-lai Xiao ◽  
Hao-ran Zhang
Keyword(s):  
Texture Feature ◽  
Google Earth ◽  
Optical Remote Sensing ◽  
Sar Image ◽  
Threshold Values ◽  
Polarimetric Sar ◽  
Texture Information ◽  
Building Area ◽  
Collapsed Buildings ◽  
Collapsed Building

<p>Rapid evaluation of building earthquake disaster information is of great significance for earthquake emergency rescue. Although polarimetric SAR has rich polarimetric information, there are still clear texture information in polarimetric SAR that could not be ignored, especially the intact artificial buildings show regular texture features in the image, and the texture distribution in the collapsed building area is disordered, so combining the texture information can also extract the building information well. In this paper, the full polarization SAR data of Yushu area in 2010 is taken as the research object, and the building area in SAR image is extracted by using the volume scattering component P<sub>V</sub> in Yamaguchi decomposition. On this basis, the intact building area and collapsed building area are extracted based on the variogram value. Comparing and analyzing the result with the intact building area is extracted by using the secondary scattering component P<sub>D</sub> in Yamaguchi decomposition. Finally, verified the accuracy by combing the optical remote sensing image after the earthquake, the extraction accuracy of intact buildings is 80.18%, collapsed buildings is 84.54%, and road water system is 77.58%.</p><p>Firstly, buildings and non-buildings are distinguished in SAR image. 100 sample matrixes are selected in building area and non-building area on P<sub>V</sub> component image respectively. After calculating the mean value of sample matrixes, the threshold values of building and non-building area are obtained based on the minimum error, and the building area and non-building area are extracted respectively according to the threshold values. Secondly, in the building area, the sample matrix of intact buildings and collapsed buildings is selected to calculate the variograms value, and then the variograms curve is drawn. When the range a = 11, the variograms value of the building area is calculated, and the FCM algorithm is used to extract the calculation results of intact buildings and collapsed buildings respectively; In order to compare and analyze the classification results, based on P<sub>D </sub>component, use K-means algorithm to extract intact buildings and the collapsed building areas are extracted separately, and the results are compared with the results based on the variogram texture feature method. Finally, the intact buildings and collapsed buildings extracted are calibrated and the extraction accuracy is calculated by combining the Google Earth historical image.</p><p>At the end of this paper, the shortcomings of extraction results based on Yamaguchi four component decomposition method and variogram method are discussed, and the idea of combining geographic information data to further improve the accuracy of earthquake damage assessment is proposed.</p>

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